Changes:

1. Implemented GibbsStep and GibbsKernel classes
2. Modified mcmc.sample function to use Gibbs sampler
3. Amended bugs in event_time_mh.py and occult_proposal.py (edge cases where tf.gathers
were overshooting the bounds of the data stuctures, not apparent on a GPU but raised on
CPU).

Changes:

1. Brought UncalibratedLogRandomWalk up to speed with latest tfp changes (stateless seed)
2. Added thin to example_config.yaml
3. Added parameter 'xi' to model to allow time-varying infec rate
4. Added parameter 'xi' to overall MCMC scheme.

Finally decided to merge the stochastic model code with the master branch.  Bye bye ODE!

Changes:

1. Beta2 introduced to measure the effect of inter-ltla commute
2. Introduced chain thinning to reduce size of posterior output

Changes:

1. Replaced 149 UTLAs with 315 LTLAs mixing matrix;
2. Wrote geometric initialisation for censored event times;
3. Modified data ingester to take PHE Anonymised Line Listing data.

Bug in discrete_markov.propagate found, introduced after changes for vectorised
likelihood.

Changes:

1. Added occult Metropolis Hastings update.
2. Factored out Categorical2 distribution for use by both event time move and occults.
3. Refactored mcmc.py script for HDF5 output purposes
4. Apply compression to HDF5 output file.

This has a considerable impact on the run speed of the algorithm, but gains in producing a
97% compression ratio for the posterior output.

The 'known' and 'occult' event tensors are added before feeding to the log_prob function.

This class was a thin wrapper around tfp.mcmc.MetropolisHastings, and was therefore just
extra code cruft.

Changes:

1. Fixed bug in inner tf.while_loop in sample()
2. Made number of event time updates per sweep a config file
parameter.

Also add a script to tune MH for event times

Cast data structures to float32 for tfd.Multinomial.log_prob
computation saves time on lgamma calculation.  Approx x1.8 speedup as
lgamma is approx linear in digits of precision required.

Results in over a x2 speedup.

Moved construction of Markov transition matrix format from
model.make_hazard.h into discrete_markov_log_prob.  Achieved a 33%
increase in run speed.

Re-wrote discrete_markov_log_prob to batch the call to
tfd.Multinomial.log_prob to avoid lgamma time.

1. Replaced TensorArray buffer with simple reduction in
discrete_markov_log_prob.
2. tf.lgamma is slow with lots of events, so cast to tf.float32 gives
a  a bit over x2 speedup.

Previously, the Events matrix expected by model.log_prob was ordered
[T, M, X] where T is the number of timpoints, M is the number of
meta-populations and X is the number of transitions.  However, it was
found more convenient to work with [M, T, X] for the purposes of data
augmentation.  This meant extra tf.transpose calls.

model.log_prob now expects [M, T, X], with any further batch
dimensions added as outer dimensions.

1. Formatting -- black style!
2. After profiling, implemented a better workaround for the
tfd.Categorical bug.  Replaced with an inherited Categorical2 class,
with overloaded log_prob function.
3. Fixed a  bug in EventTimeProposal where tfd.FiniteDiscrete was
being used on a non-strictly-increasing outcomes vector.
4. Increased number of meta-populations to 149 to mirror UK UTLA regions.